This is the first stop in the Big Data curriculum from Microsoft. It will help you get started with the curriculum, plan your learning schedule, and connect with fellow students and teaching assistants. Along the way, you'll get an introduction to working with data and some fundamental concepts and technologies for Big Data scenarios.

Learn the methods and strategies for using large-scale educational data to improve education and make discoveries about learning.

University of Pennsylvania

About this course

Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning.

In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You'll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.

The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them in Python or using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.

Learn about the methodology, practices and requirements behind data science to better understand how to problem solve with data and ensure data is relevant and properly manipulated to address a variety of real-world projects and business scenarios.

IBM

About this course

Despite and influx in computing power and access to data over the last couple of decades, our ability to use data within the decision-making process is either lost or not maximized all too often. We do not have a strong grasp of the questions asked and how to apply the data correctly to resolve the issues at hand.

The purpose of this course is to share the methods, models and practices that can be applied within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address business and real-world challenges.

You will learn how to identify a problem, collect and analyze data, build a model, and understand the feedback after model deployment.

Advancing your ability to manage, decipher and analyze new and big data is vital to working in data science. By the end of this course, you will have a better understanding of the various stages and requirements of the data science method and be able to apply it to your own work.

What you'll learn

The major steps involved in tackling a data science problem.

Why data scientists need a methodology and an approach.

What it means to understand data, and prepare or clean data

How to practice data science, including forming a concrete business question or research.